Obviously, a lot. The first image is a map of the rho-Ophiuchus molecular cloud — one of the nearest sites of star formation. The second is, apparently, random noise.

There is one important way in which these images are similar, however. Here is the histogram of the pixel brightnesses in each image:
The two images have the same distribution of pixel values. In fact, the “noise” image is simply a scrambled version of the first image. They contain identical pixels, arranged in different order.

Who cares? Well, this illustrates a common limitation to using a histogram to characterize data. It turns out that most maps of molecular clouds have similar histograms — that probably says something interesting about the physical processes that determine cloud structure. However, as the images above show, similar histograms can hide a lot of interesting differences between two data sets.

Histograms contain no information about the arrangement of pixels in an image — that’s why I could scramble the pixels in rho-Oph and preserve the histogram exactly. But there are other ways to rearrange those pixels. How about this, for example?

Again, the histogram this image is identical to the first two (download the data yourself if you don’t believe me!). The strategy for transforming an image while preserving the histogram turns out to be pretty simple. Here’s the strategy:

1) Find an image you want to match (in the case above, I used this)
2) If necessary, crop/resize the image to match the dimensions of the original image.
3) Find the location of the faintest pixel in the target image.
4) Replace this pixel with the faintest pixel in the original image.
5) Repeat for the second faintest, etc, until you replace all the pixels.

I put together a Processing applet that demonstrates this for a bunch of different images. You can find it here. This applet also shows you how the pixels in either image correspond to each other — hover your mouse over a pixel in one image to see the location of that pixel in the other.

You can even do this with color images by modifying step 4. Instead of simply substituting pixels in that step, alter the brightness to match the original image, while preserving the color. This will create a histogram of brightnesses that matches the first image, with colors that match the second.

There isn’t anything too profound going on here (although it always surprises me how well this works). But it does highlight the limitations of histograms in rather stark fashion. It’s interesting that maps of star forming regions all possess similar histograms, but this does not rule out the possibility that these regions have interesting structural differences between them. Complementary techniques are needed to tease out this possibility.

In the mean-time, enjoy this picture of the Bieber-ized rho-Ophiuchus (any guesses to what the histogram looks like?).